def scatter_hue(x, y, labels, disc=True, c_label='', norm=None):
"""Creates a wall of light curves plot with real and reconstruction
sequences, paper-ready.
Parameters
----------
x : array
data to be plotted in horizontal axis
y : array
data to be plotted in vertical axis
labels : list, optional
list with corresponding lables to be displayed as legends
disc : bool, optional
wheather the axis used for coloring is discrete or not
c_label : bool, optional
name of color dimension
Returns
-------
display figure
"""
fig = plt.figure(figsize=(12,9))
if disc:
c = cm.Dark2_r(np.linspace(0,1,len(set(labels))))
for i, cls in enumerate(set(labels)):
idx = np.where(labels == cls)[0]
plt.scatter(x[idx], y[idx], marker='.', s=20,
color=c[i], alpha=.7, label=cls)
else:
plt.scatter(x, y, marker='.', s=20,
c=labels, cmap=cmap, alpha=.7,
norm=norm)
cbar = plt.colorbar()
cbar.set_label(label=c_label, fontsize=20)
plt.xlabel('embedding 1')
plt.ylabel('embedding 2')
plt.legend(loc='best', fontsize='x-large')
plt.show()
def corr_heatmap(corr, ylab='z', xlab='z', name=''):
fig = plt.figure(figsize=(8,8))
plt.title(name)
ax = sb.heatmap(corr, vmin=-1, vmax=1, center=0,
cmap=sb.diverging_palette(20, 220, n=200),
annot=True, fmt='0.3f')
ax.set_xticklabels(
ax.get_xticklabels(),
rotation=45,
horizontalalignment='right')
ax.set_yticklabels(
np.arange(corr.shape[0]),
rotation=45,
horizontalalignment='right')
plt.ylabel(ylab)
plt.xlabel(xlab)
#plt.savefig('%s/imgs/%s.pdf' % (dpath, name),
# format='pdf', bbox_inches='tight')
plt.show()
return
def latent_space_jointdist(z, pp, par_names=''):
for k, par in enumerate(dataset.par_names):
if par in ['m_dust', 'a_max']:
label = 'log(%s)' % (par)
norm = colors.LogNorm()
else:
label = par
norm = None
g = sb.PairGrid(z, corner=False, despine=True)
g = g.map_diag(plt.hist, histtype='step', lw=1.5)
g = g.map_offdiag(plt.scatter, marker='.', s=30, alpha=.4, edgecolors='none',
color=None, c=pp[:,k], cmap=cmap, norm=norm)
cbar = plt.colorbar(shrink=5, fraction=.8, ax=g.axes[3,6], drawedges=False)
for i, j in zip(*np.triu_indices_from(g.axes, 1)):
g.axes[i, j].set_visible(False)
g = g.add_legend(loc='upper center', fontsize=20, title='',
markerscale=4, ncol=2)
cbar.set_label(label, fontsize=20)
for ax in g.axes.flat:
# This only works for the left ylabels
ax.set_ylabel(ax.get_ylabel(), fontsize='x-large')
ax.set_xlabel(ax.get_xlabel(), fontsize='x-large')
#if save_plots:
# plt.savefig('%s/imgs/z_pairplot_%s.pdf' % (path,ID),
# format='pdf', bbox_inches='tight')
plt.show()
#break
def tsne_projection(z, pp, par_names='', per=40):
tsne = TSNE(n_components=2, perplexity=per,
random_state=10, verbose=0, learning_rate=20)
z_tsne = tsne.fit_transform(z.values)
for k, par in enumerate(par_names):
if par in ['m_dust', 'a_max']:
label = 'log(%s)' % (par)
norm = colors.LogNorm()
else:
label = par
norm = None
scatter_hue(z_tsne[:,0], z_tsne[:,1],
pp[:,k], disc=False, c_label=label,
norm=norm)
return z_tsne
def umap_projection(z, pp, par_names='', nn=30, md=.12):
umapper = umap.UMAP(n_neighbors=nn, min_dist=md,
n_components=2, metric='euclidean')
z_umap = umapper.fit_transform(z.values)
for k, par in enumerate(dataset.par_names):
if par in ['m_dust', 'a_max']:
label = 'log(%s)' % (par)
norm = colors.LogNorm()
else:
label = par
norm = None
scatter_hue(z_umap[:,0], z_umap[:,1],
pp[:,k], disc=False, c_label=label,
norm=norm)
return z_umap
def pca_projection(z, pp, par_names=''):
pca = PCA(n_components=2)
z_pca = pca.fit_transform(z)
for k, par in enumerate(dataset.par_names):
if par in ['m_dust', 'a_max']:
label = 'log(%s)' % (par)
norm = colors.LogNorm()
else:
label = par
norm = None
scatter_hue(z_pca[:,0], z_pca[:,1],
pp[:,k], disc=False, c_label=label,
norm=norm)
return z_pca
def calculate_corr(latent, meta, phys_names=[]):
corr_heatmap(latent.corr(method='spearman'),
name='spearman autocorr')
latent = latent.values
spearman = pd.DataFrame(columns=phys_names,
index=range(latent.shape[1]),
dtype=np.float32)
spearman_p = pd.DataFrame(columns=phys_names,
index=range(latent.shape[1]),
dtype=np.float32)
pearson = pd.DataFrame(columns=phys_names,
index=range(latent.shape[1]),
dtype=np.float32)
pearson_p = pd.DataFrame(columns=phys_names,
index=range(latent.shape[1]),
dtype=np.float32)
for i, phy in enumerate(phys_names):
for k in range(latent.shape[1]):
#print('Latent Dim: ', k)
pcorr, pp_val = stats.pearsonr(meta[:,i], latent[:,k])
pearson.loc[k,phy] = pcorr
pearson_p.loc[k,phy] = pp_val
scorr, sp_val = stats.spearmanr(meta[:,i], latent[:,k])
spearman.loc[k,phy] = scorr
spearman_p.loc[k,phy] = sp_val
corr_heatmap(spearman, xlab='', name='spearman pairwise corr coef')
corr_heatmap(spearman_p, xlab='', name='pearson pairwise corr coef p-vals')
return spearman, spearman_p, pearson, pearson_p
def plot_scatter_stamps(xy, stamps, N=100, name='',
xlim=None, ylim=None):
fig, ax = plt.subplots(figsize=(10,10))
ax.scatter(xy[:,0], xy[:,1], marker='.', s=10,
alpha=.3)
for i, idx in enumerate(np.random.choice(xy.shape[0], N)):
imagebox = OffsetImage(stamps[idx, 0,:,:], zoom=.12,
cmap='viridis', norm=None)
ab = AnnotationBbox(imagebox, (xy[idx]), frameon=False)
ax.add_artist(ab)
if xlim and ylim:
plt.xlim(xlim)
plt.ylim(ylim)
plt.xlabel('embedding 1')
plt.ylabel('embedding 2')
plt.title(name)
plt.draw()
plt.show()
